We model trajectories as sequences of states, actions, and return-to-go tokens:While reward-to-go (or other trajectory statistics) are not necessary, we formulate the most general form to showcase how one can easily condition on additional properties of a trajectory if available. Using reward-to-go also enables us to baseline our method against previous offline-RL work [1]}.
\(\tau = \lbrace  (s_1, a_1, \hat{R}_1), \ldots , (s_T, a_T, \hat{R}_T) \rbrace ,\) 
